Definition: What is a Modern Data Stack (MDS)?

A Modern Data Stack (MDS) is a modern data architecture that comprises various components for efficiently integrating, storing and analysing data.

In contrast to traditional legacy data stacks, the MDS is flexible, scalable and cost-efficient. At the same time, it helps you to reduce the susceptibility to errors in your system landscape and can make a significant contribution to reducing costs.

What is the difference between Modern and Legacy Data Stack?

The modern data stack is more flexible and scalable than traditional data architectures. It utilises cloud-native technologies and automated data pipelines, whereas the legacy data stack is often rigid and expensive to maintain.

What advantages does MDS offer medium-sized companies?

The MDS offers numerous advantages for medium-sized companies, including increased efficiency, cost reduction and improved decision-making.

It enables a flexible data architecture that can be easily adapted to new requirements and offers powerful analysis tools that support well-founded decisions.

How long does it take to implement a Modern Data Stack?

The implementation of a modern data stack can vary depending on the scope and complexity of the project. It usually takes several weeks to months to integrate all components and optimise the systems.

Careful planning and a step-by-step implementation can help to make the process smoother and more efficient.

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Which components should not be missing in your Modern Data Stack?

1) Data Integration via API

Nowadays, every medium-sized company obtains its data from many different sources, such as CRM, ERP, website, shop, analytics or its own suppliers. Integrating this data into a target system can be a challenge.

Modern data integration solutions use APIs as well as ELT and ETL processes to bring data from different sources into a centralised system. This reduces manual work and saves time for more important tasks.

Definition: What is an API?

An API (Application Programming Interface) is a software interface that allows the connection to other software solutions and thus enables a smooth data transfer between the source and target system.

Most modern software applications today use a REST API or RESTful API in this context. This has established itself as the standard across all industries.

2) Cloud Data Warehouse

In the past, it was only possible to manage your data on your own servers. This variant is also known as “On-Premise” hosting. This required data to be stored and maintained on expensive servers.

Nowadays, we use so-called “Cloud Data Warehouses”. These modern data centres offer secure storage and fast access to data. At the same time, they are scalable so that they can grow organically with your requirements.

3) Data processing and transformation

Before you can analyse data, it has to be put into the right format. ETL and ELT processes help with this. They ensure that your data is always correct and usable.

Definition: What are ETL Processes?

ETL is the abbreviation for Extract, Transform, Load and describes a data processing procedure. Data is first extracted from various sources (Extract), then converted into a suitable format (Transform) and finally loaded into a target system (Load).

This process ensures that the data for analyses and reports is correct and consistent.

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Definition: What are ETL-Processes?

ELT stands for Extract, Load, Transform. In contrast to ETL, the data is first extracted from various sources (Extract) and loaded directly into the target system (Load).

The transformation (Transform) of the data only takes place in the target system. This approach uses the computing power of modern databases and data warehouses to process and analyse the data efficiently.

4) Data analysis and visualisation

Now it gets exciting: the analysis. Modern analysis tools allow you to visualise your data and gain deeper insights. These tools are crucial for making data-driven decisions. For example, they help you to create interactive dashboards and detailed reports tailored to different business levels.

5) DAM- & PIM-Systems

The central components in the Modern Data Stack are Digital Asset Management (DAM) and Product Information Management (PIM).

Both software solutions enable companies to centrally manage digital assets such as images, videos, documents and product data and to play them out to the right output channels (e.g. shops or marketplaces) in a channel-optimised manner via API.

In a modern data environment, digital assets play a central role as they often form the basis for marketing campaigns, product presentations and internal training courses.

DAM systems help to store these assets centrally, organise them and make them easily accessible, which increases efficiency and consistency in the handling of digital content and drastically reduces the susceptibility to errors.

• Increase efficiency:
  Automated processes reduce manual effort and minimise the susceptibility to errors.
• Ensure compliance:
  Standardised and up-to-date product information helps to fulfil regulatory requirements.
• Increase customer satisfaction:
  Consistent and accurate product information strengthens customer confidence and increases customer satisfaction.
• Reduce returns:
  By improving product data quality, you minimise incorrect data in the shop and reduce the returns rate.

Advantages of the Modern Data Stack for your Company

Increased efficiency and reduced costs

Automation and scalability are decisive factors. The Modern Data Stack helps you to reduce infrastructure costs and optimise your processes. With cloud-native solutions, you remain flexible while keeping costs under control. This means less manual work and more focus on the essentials.

Improved decision-making

Faster and more accurate analyses lead to better decisions. With modern data technology, you can gain data-driven insights and react more quickly to market changes. This supports strategic decisions and helps you to manage your company successfully.

Flexibility and adaptability

The Modern Data Stack adapts to your needs. New data sources? No problem. With a flexible data architecture, you can adapt your models and tools at any time and always stay up to date.

Data-driven Business

In order to make well-founded decisions, cross-process analyses are necessary. Linking individual tools in your Modern Data Stack allows you to do just that. In this way, you can better understand complex relationships and make data-based decisions.

What should I look out for during implementation?

• Planning
  The first step in implementing a modern data stack is careful planning. Identify your data sources and requirements and select the right tools and technologies. Thorough preparation is the key to success.
• Realisation and integration
  The integration of your software solutions requires careful planning and configuration. Ensure that all data sources are seamlessly integrated and that employees are trained accordingly. Change management is crucial here to promote the acceptance of new technologies.
• Operation and maintenance
  After implementation, it is important to regularly monitor and optimise all processes. This includes updates, scaling and the continuous improvement of processes. This ensures that your Modern Data Stack is always up to date and works efficiently.

Conclusion

The Modern Data Stack is more than just a technology – it’s a game changer for SMEs. With its help, you can manage and analyse your data more efficiently, leading to better decisions and greater competitiveness.

If you would like to find out how you can map your personal Modern Data Stack with the 4ALLPORTAL, get a free initial assessment from our team of experts!